Comment by ggregoire | Hacker News Reader

Comment by ggregoire | Hacker News Reader

> 1) Machine learning may well deliver better results for questions you're already asking about data you already have, simply as an analytic or optimization technique. For example, our portfolio company Instacart built a system to optimize the routing of its personal shoppers through grocery stores that delivered a 50% improvement (this was built by just three engineers, using Google's open-source tools Keras and Tensorflow).

> 2) Machine learning lets you ask new questions of the data you already have. For example, a lawyer doing discovery might search for 'angry’ emails, or 'anxious’ or anomalous threads or clusters of documents, as well as doing keyword searches.

> 3) Third, machine learning opens up new data types to analysis - computers could not really read audio, images or video before and now, increasingly, that will be possible.

Are there any online resources to learn and implement step-by-step some ML to solve one of these use cases (or other ones)? Like a GitHub repo with data samples, a programming environment and then a step-by-step guide to solve trivial but real business problems?

I am working through Google's machine learning crash course.

https://developers.google.com/machine-learning/crash-course/...

Tensorflow sprays a lot of calculus. The idea is that all your known quantities ("features") become terms in an n-dimensional polynomial. The act of "training the model" is finding minima by traversing the negative gradient (the terms of the partial derivatives of any point when projected as a vector at that point).

I'm glad I had calc 3, even if that was only 3-dimensional.

Technically, it's not a polynomial as you can have nonlinear activation functions, such as the ReLU function. There's no polynomial that's equal to a network with ReLU activations (although, of course, a sufficiently large polynomial could come arbitrarily close).

I would state that a neural network is a large, complicated, differentiable function, and the beauty of deep learning is that it turns out that by doing optimization that's derived from basic calculus, you can optimize this complicated function to do surprisingly useful things.

I'm not at the point where I can debate technicalities yet, alas.

When you get there you probably still shouldn't do it.

You might find http://www.fast.ai/ useful. Depending on your learning style, their courses can either be amazing or somewhat annoying. Their library includes jupyter notebooks so that you can work through the examples.

Hey, I'm involved with a startup and we're aiming to just that -- provide a 0 to job-ready experience in machine learning. We're more of a paid, integrated experience than a repo with notebooks though.

Here's our data science path - https://www.dataquest.io/path/data-scientist

Every course has a project using real datasets - https://www.dataquest.io/projects

Check out Commonlounge: https://www.commonlounge.com/discussion/8d5612f5517944059a61...

The courses have hands-on assignments that make you work through real-world problems.

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